# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from unittest import mock

import numpy as np
import pandas as pd
import pytest

import autokeras as ak
from tests import utils


def test_raise_error_unknown_str_in_col_type(tmp_path):
    with pytest.raises(ValueError) as info:
        ak.StructuredDataClassifier(
            column_types={"age": "num", "parch": "categorical"},
            directory=tmp_path,
            seed=utils.SEED,
        )

    assert 'Column_types should be either "categorical"' in str(info.value)


def test_raise_error_unknown_name_in_col_type(tmp_path):
    with pytest.raises(ValueError) as info:
        ak.StructuredDataClassifier(
            column_types={"age": "numerical", "parch": "categorical"},
            column_names=["age", "fare"],
            directory=tmp_path,
            seed=utils.SEED,
        )

    assert "Column_names and column_types are mismatched" in str(info.value)


@mock.patch("autokeras.AutoModel.fit")
@mock.patch("autokeras.AutoModel.evaluate")
def test_structured_clf_evaluate_call_automodel_evaluate(evaluate, fit, tmp_path):
    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)

    auto_model.fit(x=utils.TRAIN_CSV_PATH, y="survived")
    auto_model.evaluate(x=utils.TRAIN_CSV_PATH, y="survived")

    assert evaluate.is_called


@mock.patch("autokeras.AutoModel.fit")
@mock.patch("autokeras.AutoModel.predict")
def test_structured_clf_predict_csv_call_automodel_predict(predict, fit, tmp_path):
    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)

    auto_model.fit(x=utils.TRAIN_CSV_PATH, y="survived")
    auto_model.predict(x=utils.TEST_CSV_PATH)

    assert predict.is_called


@mock.patch("autokeras.AutoModel.fit")
def test_structured_clf_fit_call_auto_model_fit(fit, tmp_path):
    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)

    auto_model.fit(
        x=pd.read_csv(utils.TRAIN_CSV_PATH).to_numpy().astype(np.unicode)[:100],
        y=utils.generate_one_hot_labels(num_instances=100, num_classes=3),
    )

    assert fit.is_called


@mock.patch("autokeras.AutoModel.fit")
def test_structured_reg_fit_call_auto_model_fit(fit, tmp_path):
    auto_model = ak.StructuredDataRegressor(directory=tmp_path, seed=utils.SEED)

    auto_model.fit(
        x=pd.read_csv(utils.TRAIN_CSV_PATH).to_numpy().astype(np.unicode)[:100],
        y=utils.generate_data(num_instances=100, shape=(1,)),
    )

    assert fit.is_called


@mock.patch("autokeras.AutoModel.fit")
def test_structured_data_clf_convert_csv_to_df_and_np(fit, tmp_path):
    auto_model = ak.StructuredDataClassifier(directory=tmp_path, seed=utils.SEED)

    auto_model.fit(
        x=utils.TRAIN_CSV_PATH,
        y="survived",
        epochs=2,
        validation_data=(utils.TEST_CSV_PATH, "survived"),
    )

    _, kwargs = fit.call_args_list[0]
    assert isinstance(kwargs["x"], pd.DataFrame)
    assert isinstance(kwargs["y"], np.ndarray)
